Caching Strategy

Caching operates at multiple layers: browser caches, CDN edge caches, application-level caches like Redis or Memcached, and database query caches. Each layer trades storage space and consistency for speed. Common patterns include cache-aside where the application manages cache population, write-through where writes update both cache and database, and write-behind where cache writes are asynchronously flushed to storage.

For AI product teams, caching is one of the most impactful performance optimizations because model inference is computationally expensive. Caching identical or semantically similar queries can reduce inference costs by 30-70% while dramatically improving response times. Growth teams should advocate for aggressive caching of AI responses for common queries, cached model outputs for popular content recommendations, and edge-cached personalization data. The challenge is balancing freshness with performance: if a recommendation cache updates every hour, users see stale suggestions that do not reflect recent behavior. Defining cache TTLs based on how quickly the underlying data changes and how sensitive the use case is to staleness is a critical design decision.